We would never send a rocket to deliver a sandwich. So why are we sending massive, general-purpose AI brains to do tiny, predictable tasks in our systems? In our latest article, we examine the growing mismatch between model size and business need. Frontier models are extraordinary, but they are not always economical, private, or fast. Every unnecessary call to a large model increases inference cost, introduces latency, and raises legitimate questions about data exposure and compliance. For many operational tasks such as classification, extraction, tagging, or structured summarisation, smaller language models can perform with precision at a fraction of the cost and with significantly lower response times. When deployed closer to the edge or within controlled environments, they also offer a more defensible privacy posture. The next generation of AI architecture will not revolve around a single, dominant brain. It will look more like a model fleet. Lightweight specialists handling pred...